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Assessing Progress towards Public Health, Human Rights, and International Development Goals Using Frontier Analysis

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  • Jeanne Luh
  • Ryan Cronk
  • Jamie Bartram

Abstract

Indicators to measure progress towards achieving public health, human rights, and international development targets, such as 100% access to improved drinking water or zero maternal mortality ratio, generally focus on status (i.e., level of attainment or coverage) or trends in status (i.e., rates of change). However, these indicators do not account for different levels of development that countries experience, thus making it difficult to compare progress between countries. We describe a recently developed new use of frontier analysis and apply this method to calculate country performance indices in three areas: maternal mortality ratio, poverty headcount ratio, and primary school completion rate. Frontier analysis is used to identify the maximum achievable rates of change, defined by the historically best-performing countries, as a function of coverage level. Performance indices are calculated by comparing a country’s rate of change against the maximum achievable rate at the same coverage level. A country’s performance can be positive or negative, corresponding to progression or regression, respectively. The calculated performance indices allow countries to be compared against each other regardless of whether they have only begun to make progress or whether they have almost achieved the target. This paper is the first to use frontier analysis to determine the maximum achievable rates as a function of coverage level and to calculate performance indices for public health, human rights, and international development indicators. The method can be applied to multiple fields and settings, for example health targets such as cessation in smoking or specific vaccine immunizations, and offers both a new approach to analyze existing data and a new data source for consideration when assessing progress achieved.

Suggested Citation

  • Jeanne Luh & Ryan Cronk & Jamie Bartram, 2016. "Assessing Progress towards Public Health, Human Rights, and International Development Goals Using Frontier Analysis," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-16, January.
  • Handle: RePEc:plo:pone00:0147663
    DOI: 10.1371/journal.pone.0147663
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    References listed on IDEAS

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    1. World Bank, 2015. "World Development Indicators 2015," World Bank Publications - Books, The World Bank Group, number 21634.
    2. Joe Zhu, 2014. "Data Envelopment Analysis," International Series in Operations Research & Management Science, in: Quantitative Models for Performance Evaluation and Benchmarking, edition 3, chapter 1, pages 1-9, Springer.
    3. Cook, Wade D. & Tone, Kaoru & Zhu, Joe, 2014. "Data envelopment analysis: Prior to choosing a model," Omega, Elsevier, vol. 44(C), pages 1-4.
    4. Seiford, Lawrence M. & Thrall, Robert M., 1990. "Recent developments in DEA : The mathematical programming approach to frontier analysis," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 7-38.
    5. Wilson, Paul W, 1993. "Detecting Outliers in Deterministic Nonparametric Frontier Models with Multiple Outputs," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(3), pages 319-323, July.
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    Cited by:

    1. Hainan Guo & Yang Zhao & Tie Niu & Kwok-Leung Tsui, 2017. "Hong Kong Hospital Authority resource efficiency evaluation: Via a novel DEA-Malmquist model and Tobit regression model," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-24, September.
    2. Stefanie Schwemlein & Ryan Cronk & Jamie Bartram, 2016. "Indicators for Monitoring Water, Sanitation, and Hygiene: A Systematic Review of Indicator Selection Methods," IJERPH, MDPI, vol. 13(3), pages 1-15, March.

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